LangSmith/Docs/Observability Studio/LangGraph assistant configuration

• Observability in Studio — full example configuration

  • Popped out from 2025-12-24 (was under LangChain/Ecosystem) so the LangGraph assistant configuration recipe is one link away.
  • See also langgraph/Docs/Cloud/How To/Prompt Engineer in LangGraph Studio for langgraph_nodes, langgraph_type, and how Studio binds fields to nodes.
  • For some reason it's always so hard for me to find this page, which is, to the best of my knowledge, one of the only pieces of documentation that shows how to configure LangGraph assistants using Annotated[Literal[...]] and json_schema_extra.

  • Pydantic and dataclass examples

    • python

      ## Using Pydantic
      from pydantic import BaseModel, Field
      from typing import Annotated, Literal
      
      class Configuration(BaseModel):
          """The configuration for the agent."""
      
          system_prompt: str = Field(
              default="You are a helpful AI assistant.",
              description="The system prompt to use for the agent's interactions. "
              "This prompt sets the context and behavior for the agent.",
              json_schema_extra={
                  "langgraph_nodes": ["call_model"],
                  "langgraph_type": "prompt",
              },
          )
      
          model: Annotated[
              Literal[
                  "anthropic/claude-sonnet-4-5-20250929",
                  "anthropic/claude-haiku-4-5-20251001",
                  "openai/o1",
                  "openai/gpt-4o-mini",
                  "openai/o1-mini",
                  "openai/o3-mini",
              ],
              {"__template_metadata__": {"kind": "llm"}},
          ] = Field(
              default="openai/gpt-4o-mini",
              description="The name of the language model to use for the agent's main interactions. "
              "Should be in the form: provider/model-name.",
              json_schema_extra={"langgraph_nodes": ["call_model"]},
          )
      
      ## Using Dataclasses
      from dataclasses import dataclass, field
      
      @dataclass(kw_only=True)
      class Configuration:
          """The configuration for the agent."""
      
          system_prompt: str = field(
              default="You are a helpful AI assistant.",
              metadata={
                  "description": "The system prompt to use for the agent's interactions. "
                  "This prompt sets the context and behavior for the agent.",
                  "json_schema_extra": {"langgraph_nodes": ["call_model"]},
              },
          )
      
          model: Annotated[str, {"__template_metadata__": {"kind": "llm"}}] = field(
              default="anthropic/claude-3-5-sonnet-20240620",
              metadata={
                  "description": "The name of the language model to use for the agent's main interactions. "
                  "Should be in the form: provider/model-name.",
                  "json_schema_extra": {"langgraph_nodes": ["call_model"]},
              },
          )